New Credit: How Data Science is Changing Lending

By: James Jasper and Jared Maslin

Final Draft: 5/04/2015

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Introduction

Since the financial crisis, low-income growth and persistent poor credit has limited many

from achieving stability in their lives. These dual limitations has led to their being 50 million

individuals deemed “unscorable” by traditional credit methods – effectively freezing them out of

the market for home loans, car loans, and access to credit cards.1 Alongside these developments,

technical hurdles to accessing social networks and other methods of rating an individual’s ability

to repay a loan have decreased. Data science is now being used to extend a variety of alternative

credit instruments to these underserved individuals.

While we applaud the effort to reach this population with needed financial services and

new options for credit and debt consolidation, using data science’s methodologies in lending is

an area ripe for abuse, likely illegal due to violations of the Equal Credit Opportunity Act and the

Fair Credit Reporting Act, and discriminatory. In this paper we will examine how the current

state of credit markets came to exist, how data science is now involved in lending decisions, and

the associated regulatory and ethical considerations of its use. We find the current justifications

of consumer credit practices by non-insured institutions to be unsettling due to the lack of

transparency in their credit decisions and the consistently higher than market rates they charge to

borrowers. At the end of this paper we suggest improvements to current legislation that can

protect consumers and companies alike.

History of Credit

Credit is the foundation of modern society. The consistent ability to rate a borrowers ability to

repay a loan reduces risk in the overall economy and provides lenders a relatively safe store for

1 Crosman, Penny and Andy Peters. “New Underbanked FICO Score Faces Old Banker

Skepticism”. April 2, 2015. http://www.americanbanker.com/news/bank-technology/new-

underbanked-fico-score-faces-old-banker-skepticism-1073600-1.html.

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excess savings. More importantly, access to credit is the foundation of a fair and equal society. A

critical component of lending decisions, the concept of credit has existed since the ancient

Greeks.2 The extension of credit is based on the ability of an individual to repay – originally a

decision was based on the relations of that individual, amount they desired to borrow, and

income or assets they had against the loan. A rudimentary credit score was used to define the

terms of any deal. As such, credit and credit scoring evolved and became more sophisticated to

accommodate changes in the banking and to address the needs of society. Many of the laws that

advanced the rights of minorities centered on this access; the Fair Housing Act (FHA) of 19683

and the Community Reinvestment Act (CRA) of 19774 both set strict requirements on the ways

credit was extended to protected classes and the obligations of banks to lend into the

communities where they were based.

However, since the passage of both the FHA and CRA technology has rapidly outstripped

the ability of the government to regulate the banking industry. The advent of the FICO score in

1989 by Fair, Isaac, and Company standardized the assessment of the ability to repay. The use of

the FICO score greatly reduced the abuses of the credit system as it removed the ability of banks

to use arbitrary interviews to deny credit. Additionally, the proliferation of the Internet

broadened the markets available to banks. As such, a local bank could now operate across state

lines for clients while providing the same level of service. Traditional methods of client relation

building broke down as ease of access to information increased. Large banks became

increasingly centralized, as they would lend to any qualified borrower.

2 Homer, Sidney. History of Interest Rates: 2000 B.C. to the Present. New Jersey: Rutgers U.P.,

1963. 3 42 U.S. Code Chapter 45, Subchapter I. https://www.law.cornell.edu/uscode/text/42/chapter-

45/subchapter-I.

4 12 U.S. Code Paragraph 2901. https://www.law.cornell.edu/uscode/text/12/2901.

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This growth and acceleration of the banking sector carried certain risks that were not

fully understood until 2007 when the housing market collapsed. While credit had built a great

number of homes, it just as swiftly resulted in a collapse and freeze of credit markets from which

we have yet to fully recover.5 For consumers, the results of this collapse have been two-fold:

1. The portion of the population with subprime or near prime credit rose to 56% of the

population.6

2. The portion of unbanked individuals stands at 7.7% and underbanked individuals at 20%.

To put that in perspective, nearly half of credit users would have difficulties getting a home or

auto loan. Nearly one-in-four do not have access to credit cards, checking or savings accounts,

and as a result are stuck in high cost alternative financial services.7

The combined effects of consistently depressed credit scores and a mistrust of traditional

financial institutions have resulted in a fundamental shift in how lending services are being

provided to lower income individuals. Alternative Financial Services (AFS), services provided

5 As defined by failure to achieve stable job creation and income growth which are defined by

the Federal Reserve Board as the key elements influencing their decision to keep interest rates at

historic lows. 6 Defined as consumers with a 700 or lower FICO score. For a full list of data: “Financial Assets

and Income: Consumers with Subprime Credit.” 2015 CFED Survey.

http://scorecard.assetsandopportunity.org/latest/measure/consumers-with-subprime-credit. For a

fuller explanation see: 2013 FDIC National Survey of Unbanked and Underbanked Households.

October 2014. https://www.fdic.gov/householdsurvey/2013report.pdf 7 “Alternative Financial Services: A Primer”. FDIC Quarterly 2009, Vol 3:1, 39-47.

https://www.fdic.gov/bank/analytical/quarterly/2009_vol3_1/FDIC140_QuarterlyVol3No1_AFS

_FINAL.pdf

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outside of federally insured banks and thrift institutions, now account for $320 billion dollars of

transactions annually8:

Furthermore, traditional institutions have little interest in attempting to acquire unbanked and

under banked individuals. There is a woeful lack of educational material, understanding of

financial services, and access to checking and savings accounts to this traditionally underserved

population.9

As a result of these shifts, there has been a significant focus by private equity and venture

funded firms to provide services to this underserved population. However, with extension of

8 For a complete assessment of the impact of Alternative Financial Services see the FDIC’s

primer on AFS above. 9“FDIC Survey of Banks’ Efforts to Serve the Unbanked and Underbanked: Executive Summary

of Findings and Recommendations.” Feb. 2009.

https://www.fdic.gov/unbankedsurveys/unbankedstudy/FDICBankSurvey_ExecSummary.pdf

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credit through AFS style investment vehicles are high interest that further disadvantage

borrowers who already have low incomes and difficulty repaying.10 Data science has been on the

forefront of these lending decisions – using payment history and open lines of credit to determine

a borrower’s likelihood to repay. However, more recent advances in the field of data science

have allowed lenders to reach much deeper into an applicant’s histories to determine ones

creditworthiness.

Credit and Lending

Statistics and probability has always been used in lending to some extent. The market for debt is

determined by future expected returns and the related risks to a given individual or company.

Only recently, however, has statistics and probabilities been used at the individual level through

the advent of a standardized FICO score from the Fair Isaac Corporation in the 1980’s.

Since the advent of a numeric score for credit reporting, the practice has become

widespread with a variety of other companies offering similar scores.11 The premise behind these

scores is the use of predictive modeling to identify the risks of default of the borrower on a loan.

As such, different lenders can establish different criteria in terms of trade lines and payments on

those lines for loans based on the risks they are willing to take and price their products

effectively for that risk.12 There are positive and negative aspects to using a unified approach to

risk scoring. Positive results include:

10 Klepper, David. “NY lawmakers examine subprime auto loans.” April 23, 2015.

www.washingtontimes.com/news/2015/apr/23/ny-lawmakers-examine-subprime-auto-loans/ and

Harnett, Sam. “One Uber Driver’s Story: How He Was Trapped by Auto-Loan Program.” April

24, 2015. ww2.kqed.org/news/2015/04/24/one-uber-drivers-story-how-he-was-trapped-by-auto-

loan-program. 11 “The Impact of Differences between consumer- and creditor-purchased credit scores.” CFPB.

July 19, 2011. http://files.consumerfinance.gov/f/2011/07/Report_20110719_CreditScores.pdf 12 It should be noted that these standards are often opaque themselves: many large banks require

multiple trade lines open for multiple years. These requirements aren’t typically disclosed

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Ease of comparison between borrowers.

Effective risk based pricing for individuals.

Simplified transaction cost through ease of access to credit history across lenders.

High degree of regulation with Fair Credit Reporting Act Reg. B and Reg. V.

Negative outcomes include13:

Heavily dependent on length of credit history (35% of score).

o Bias against younger people and people new to credit instruments.

Does not account for assets or income, only payments.

Focus on percent utilization of available credit as opposed to ability to afford spending

levels.

Minority neighborhoods average 550 credit scores, non-minority neighborhoods average

a 700 score.

As previously noted, a poor credit score drastically increases the cost of available financing and

can freeze someone out of traditional financial institutions forcing them into costly alternative

financial services.

New Risk Calculations Using Data Science

The combination of the effects of a large population of subprime borrowers and a large

group of unbanked individuals has allowed more advanced methods of data science to make

inroads into lending. This significant opportunity has attracted many data science and analytics

upfront to a prospective borrows, but would be disclosed in an adverse action notice as described

by Regulation B of the Fair Credit Reporting Act: 12 CFR 202—Equal Credit Opportunity

(Regulation B). Office of the Comptroller of the Currency. U.S. Department of Treasury. July

15, 2011. http://www.occ.gov/news-issuances/bulletins/2011/bulletin-2011-39.html. 13 Meeks, John. “FICO Scores”. FDIC. October 28th, 2011.

http://www.wvasf.org/presentation_pdfs/John_Meeks_-

_WV_Asset_Building_Charleston_102811.pdf

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companies into consumer lending. These companies vary in style and organization but their

overall strategies are clear: increase the number and the variety of parameters in a lending

decision to reduce the risks.

As far as lending goes, the number of companies has rapidly multiplied: AvantCredit,

Moven, Kabbage Inc., Kreditch, LendUp, Finca, Neo, ZestFinance, LendingClub, and Lenddo

are just a few companies adopting this approach. While some focus on consumer lending such as

ZestFinance and LendingClub, others like Kabbage, lend to small businesses. Data gathering

focuses range from examining LinkedIn and Facebook profiles for number of connections to

gaining direct access to bank accounts and spending habits.

To a certain extent, such activities are a natural outgrowth of the growth of available data

online. In a business context, Yelp reviews as well as web presence are important factors in the

success of a modern business. However, it becomes much more tenuous to suggest that lending

decisions can or should be based on online profiles. Recent articles have noted the broad fraud

that social media has allowed; fake profiles abound, look legitimate, and are difficult to stop.14

As such, there’s a large possibility of fraud that could be perpetrated by borrowers to dupe

lenders. Additionally, legal and ethical considerations for the lenders are a serious concern as

they should be able to justify lending decisions that they are making for individuals.

Legal Concerns

At the core of legal considerations around the extension of credit is the Fair Credit

Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA). The FCRA stipulates that

credit reporting to consumer credit agencies must be accurate and the note of adverse credit

14 Bilton, Nick. “Social Media Bots Offer Phony Friends and Real Profits.” Nov. 19th, 2014.

http://www.nytimes.com/2014/11/20/fashion/social-media-bots-offer-phony-friends-and-real-

profit.html?_r=0.

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decisions, in the form of a denial of financing, must be provided with reasoning. Additionally,

the ECOA provides that there should be no discrimination in access to financing based on age,

race, religion, or any other arbitrary factor. The FCRA provides a broad definition of consumer

report agency or consumer report as well:

FCRA, 15 U.S.C. § 1681a(d): any written, oral, or other communication of any

information by a consumer reporting agency on a consumer's credit worthiness,

credit standing credit capacity, character, general reputation, personal

characteristics, or mode of living which is used or expected to be used or

collected in whole or in part for the purpose of serving as a factor in establishing

the consumer's eligibility for (A) credit or insurance to be used primarily for

personal, family, or household purposes; (B) employment purposes; or (C) any

other purpose authorized under section 604.

Information in such reports should be accurate and redress exists for consumers who

notice inaccurate credit information on such reports. The number of organizations that could fall

under this FCRA umbrella is quite large.

Based on the known methods of how these new firms extend credit, some major legal

concerns come to the fore. First and foremost is the adherence to the FCRA and ECOA in whole.

While this may seem to be simple, the Regulation B of FCRA significantly complicates the

extension of credit in this way. Regulation B states that after an adverse decision is made proper

notice of why credit was not provided should be provided once receipt of a full application has

been achieved. Given that many of these algorithms examine large numbers of data points, such

a provision would be difficult to provide – stating that the user did not have enough Facebook

friends while an equivalent user may have qualified with the same number of friends would not

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be an understandable difference. In fact, such a decision would be considered arbitrary as one

would need to prove that this factor significantly increased risks to the lender in a measurable

way.

To further complicate matters, if social networks are being examined, this data could

violate the ECOA. For example, research has shown that social networks are vitally important in

determining future earnings. If a company is now deciding to provide credit based on social

networks, there has shown to be significantly more earning potential among white individuals

due to their networks than African Americans.15 Additionally, social networks self-segregate

along racial lines.16 One could violate the ECOA by examining a social network, and while race

may not be openly available, associated information would allow the inference of this

information. Rather than making a credit decision on concrete facts, the results would be based

on inferential information – another violation of current legal statures. Such data mining would

be in violation of the ECOA as it clearly defines a lending decision along racial lines as illegal.

However, these concerns must be balanced against the benefits that these services

provide to unbanked and underbanked individuals. For instance, if no one can achieve financing

through traditional methods, the legal considerations that the FCRA and ECOA are supposed to

address have failed to succeed. The question then becomes to what extent should innovation in

finance be allowed versus how much current regulations need to be changed to serve other

individuals.

15 Flores-Yeffal, Nadia Y. and Li Zhang. “The Role of Social Networks in Determining

Earnings: A Comparison Analysis of Four Racial and Ethnic Groups.” Sociology Mind: Vol 2, 5,

2012. 235-246. http://www.scirp.org/journal/PaperDownload.aspx?paperID=18447 16 American Values Survey. October 29th, 2013. http://publicreligion.org/research/2013/10/2013-

american-values-survey/#.VT2jNK1Viko

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In addition to the legal considerations, much of this data analysis crosses the lines into

research on human subjects. As such, there are ethical concerns in additional to legal

requirements. We try to apply the Belmont Report to these concerns.

Applying the Belmont Report

The Belmont Report, developed by the National Commission for the Protection of

Human Subjects of Biomedical and Behavioral Research, puts forth a framework of ethical

guidelines that are meant to aid in ensuring the protection of human subjects in research. Despite

being developed in 1979, the report has been upheld over time as an essential rubric in all areas

of research. Data science, in many ways, employs many of the same experimentation and

procedural methods as more traditional areas of research, such as physiological or medicinal

research. When medical research might involve testing the effects of a specific drug or treatment

onto a set of subjects, data science research can also involve the use of personal data from human

subjects, whether in a financial, psychological, or other realm. With this in mind, this paper will

consider the potential applicability of some key aspects from the Belmont Report onto the

current framework of social data usage in the lending process.17

The Belmont Report details three primary areas of ethical focus concerning what is

necessary to ensure an ethical research practice including respect for persons, beneficence, and

justice.

Respect for Persons

The principle of “respect for persons” focuses on the relative capacity or vulnerability of

each individual subject being included in a research project. Tantamount to this is the capacity to

17 National Commission for the Protection of Human Subjects of Biomedical and Behavioral

Research; “Ethical Principles and Guidelines for the Protection of Human Subjects of Research”;

http://www.hhs.gov/ohrp/humansubjects/guidance/belmont.html., April 18, 1979.

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make decisions or contemplate issues that could arise both before and during research. In

pharmaceutical research, this could be viewed as the capacity of a trial participant in considering

the risks and benefits of their involvement in a research project and, if necessary, contemplation

of removal from the project should they deem their participation to be inappropriate or harmful

in any way.

In applying this principle to the realm of lending, one might view respect as a necessity

to assess each participant’s potential ignorance to an institution’s use of this data and the impact

that it can have on their lending decision. While some subjects may be fully aware of the

potential consequences of the use of their social data in lending decisions, others may not fully

comprehend the impact that it can have, depending upon what the lender finds and how they

choose to interpret the information.

Take AvantCredit, for example – Avant offers consumer lending to underserved

borrowers through the use of publically-available information. Avant uses consumer credit

information, which is no different than the vast majority of consumer lenders in the market

today. Where Avant diverges in practice is their use of social data and user action data, tracking

their activity online (i.e., social media websites) and the network of friends, colleagues, peers

that they have constructed through the use of social media applications. While being “personal

data” in the minds of many social media users, the data itself is viewed as publically-available to

many entities, as almost anyone can go online and retrieve the information that Avant is using in

its lending decisions.18

18 Rakesh, Chitra. “Online lender AvantCredit secures $34M in funding”. Venture Beat.

http://venturebeat.com/2013/05/09/online-lender-avantcredit-secures-34m-in-funding/. May 9,

2013.

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In applying the Belmont standard of respect for persons, one might wonder how Avant is

ensuring that potential borrowers are fully aware and capable of contemplating the use of their

social data in their consumer lending determinations. Are borrowers made aware prior to being

evaluated for a loan or is this something in the fine print shown after the fact? If such a gap were

to exist, companies like Avant would need to address the clarity with which it communicates the

benefits and risks to borrowers of using social data in their decision making.

Beneficence

The principle of beneficence is a fairly simple one, focusing on the reduction of harm to

research subjects and the maximizing of benefits to subjects, to the fullest extent possible. In

many ways, beneficence runs alongside a doctor’s Hippocratic Oath of “do no harm”. The

additional aspect of maximizing the benefit can be thought of as the researcher’s responsibility to

consider all potential benefits of a practice, regardless of the party or parties that could become

beneficiaries.

Taking Avant as an example again, there should be an expectation that Avant considers

and documents the potential risks and benefits to use of social data in lending determinations.

Being a for-profit business, there could be a tendency for management to observe the ways in

which using borrower-related data could increase their overall profitability and nothing further.

Doing so would not satisfy the principle of beneficence, though, as one must also consider the

benefits and risks of the practice for all parties that could potentially be impacted. Could this

practice result in deceptive lending practices or in egregious lending products being offered to

consumers? Could this practice increase economic risk in ways that subprime lending has been

shown to impact economies in the recent history? In these ways, Avant should be responsible for

a full consideration of the entire impact set surrounding their lending practices.

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Justice

The principle of justice can be seen as a detailed principle with several facets to consider.

At a high level, the principle can be viewed as a set of considerations in spreading the respective

benefits and burdens across the parties involved. The set of considerations include the use of

equitable distribution, need-based consideration, merit-based consideration, levels of relative

effort, and levels of relative societal contribution. In many ways, each of these considerations

points to the relative fairness of a given research or business practice. Ensuring justice in a

lending practice would require that each institution exercise discipline in determining how to

execute their business plan and the impacts of its practice onto its subjects and society, as a

whole.

For an example, let us consider Lending Club, which originated as a social application

within Facebook’s platform. Lending Club uses a peer-to-peer lending process, acting as an

intermediary between potential public lenders and borrowers. In doing so, the principle of justice

requires that Lending Club exercise control in selecting its consumer base according to their

respective needs and merit-based attributes. For this, they could observe the FICO credit score

and income of potential borrowers, while assessing the availability of competing institutions’

financial products to potential borrowers in the market. This will allow for Lending Club to

ensure that it is applying an ethical focus to its operations and to its selection of business

partners.19

In addition, Lending Club must ensure an equitable distribution of the benefits between

the business, its customers, and society, as a whole. One might contemplate whether lenders are

making a fair return on their investment, relative to that which Lending Club secures through

19 Lending Club. “How does an online credit marketplace work?”

https://www.lendingclub.com/public/how-peer-lending-works.action. 2015.

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each transaction. Also, are borrowers being given a fair interest rate for their loan or is Lending

Club gouging into the pockets of vulnerable or desperate borrowers? Even further, how does

Lending Club’s existence alter the lending market for consumers? Do Lending Club’s practices

oversaturate the market or make it more difficult for consumers to obtain lending from other

commercial sources (i.e., does it drive up rates from other institutions)? The principle of justice

would demand that Lending Club consider and implement an equitable distribution of the

business operational detriments and benefits amongst all parties that are potentially impacted.

Limitations of the Belmont Report as a Business Framework

While the Belmont Report provides a very useful framework in considering the ethical

aspects of a research method for human subjects, its application to businesses are somewhat

limited on a few fronts. For one, many research efforts are not implicitly driven by profitability

as a key motive. Conducting research on the quality of learning for students in various regions or

learning environments may not be principally driven by a desire to make money, in one way or

another. For example, enhancing educational experiences for students and teachers alike could be

a driving motivation to conduct research of this nature. In considering for-profit businesses,

though, profitability is often a guiding principle that leads all business-related discussions of

products, processes, or people.

Matters of social responsibility are debated heavily, with profitability going head-to-head

with a perceived obligation that businesses serve the community first and foremost. The Belmont

Report details matters of accountability and responsibility, but not necessarily those of

profitability. In the lending market today, lenders may dispute that the act of lending places the

majority of risk onto the business’s shoulders, justifying the distribution of profits or benefits to

the lender before the borrower. Even further, the use of social data can be defended as a manner

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of protecting the business from deception on the part of potential borrowers. One might argue

that through the use of social data, lenders can uncover details about potential borrowers that

they would otherwise wish to conceal when securing a loan. These matters can often transcend

the confines of the Belmont Report and require additional consideration in order to understand

the risks and benefits of a business practice.

Second, many research practices observe risks to subjects as those that can manifest

themselves during the course of a project. For example, if a researcher wants to understand how

multi-tasking affects productivity and seeks to construct an experimental design to attack this

problem, the researcher may consider potential harms like the physical fatigue or

mental/psychological strain that subjects might experience. It might be considered less relevant

to look further down the road to how subjects might be affected one, two, or twenty years down

the road, and in many ways, there isn’t necessarily a reason to do so. With business practices like

consumer lending, the introduction of potentially subjective social data in lending determinations

could have drastic, long-term impacts on how lending is structured in the future and how that

structure impacts the financial market. As seen in recent years, subprime lending practices can

cripple an economy if left unchecked or unregulated. While seemingly harmless, the Belmont

Report lacks an infrastructure to define long-term impacts that can loosely stem from the use of

social data in lending decisions.

A Fine Line – Social Data and Lending Decisions

The lending industry, as it exists today, is intensely regulated and monitored by the

Federal Government. Fair Lending laws like the Equal Opportunity Act 20 and the Truth in

2015 U.S.C. 1691 et seq., 12 C.F.R. pts. 202 and 1002 and 12 C.F.R. 701.31 (NCUA)

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Lending Act21 seek to protect potential borrowers through ensuring equality and clarity in the

offering and consideration of lending opportunities. The process for seeking out and engaging

potential borrowers is restricted, both in aspects of timing and method. The use of social data,

though, throws a bit of a wrench into the current legal framework.

By nature, social data regularly includes personal details such as race, physical build

(through images), marital status, sexual orientation, religious beliefs, and personal hobbies. And

these are just a few of the possible data points that can be obtained simply through the scraping

of a borrower’s social media homepage. While being publically available, the information

crosses the proverbial “line” and into the realm of protected parties. This begs the question of

how social media data can be used, yet controlled in a way that prevents any kind of

discrimination or bias from being introduced. How might this information be used to target

vulnerable groups or exclude those that are deemed “unfit” for lending on the basis of social

media data? And unless the use of such information is recorded and tracked to an incredibly

detailed degree (which likely wouldn’t be cost-effective or even feasible), how can regulators

ensure that the information is being used in a legal and ethical manner?

The Federal Trade Commission (FTC) Act also presents a potentially troublesome legal

hurdle to consider. Section 5 of the FTC Act protects against “unfair or deceptive acts or

practices in or affecting commerce.”22 One potential concern here is that it can be exceedingly

difficult to prove the intent and eventual use of certain pieces of data. When a researcher scrapes

profile information off of a social media site, it’s incredibly likely that included in their data set

21 15 U.S.C. 1601 et seq.; 12 C.F.R. pts. 226 and 1026 22 16 C.F.R. Part 255, FTC “Guides Concerning the Use of Endorsements and Testimonials in

Advertising”.https://www.ftc.gov/sites/default/files/attachments/press-releases/ftc-publishes-

final-guides-governing-endorsements-testimonials/091005revisedendorsementguides.pdf. 2009.

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is a mix of personal or potentially discriminatory data. Once that data is gathered, how can

businesses be monitored or effectively regulated to ensure that discrimination or unethical bias in

commerce never come into play? And even if certain data points are gathered, but not

specifically used in decision making, can the public ever truly be comfortable that the process is

devoid of bias and that the data isn’t “tainted” in some way?

Regulatory Compliance and Dubious Legality

Being such a highly regulated industry, each individual lending decision comes with a

laundry list of requirements and expectations in terms of what is communicated, how it is

communicated, and to whom it is communicated. The formal reasoning for a judgment must fit

into a narrow lane, as must the manner in which that decision is documented. But with this

comes an innate weakness to the system – regulators have neither the time, nor the resources to

audit or inspect lending processes beyond the specific wording in legislation. As such, lenders

must focus heavily on “to-the-letter” compliance in their daily operations. What this also means,

though, is that there is likely opportunity to identify loopholes or flexibilities within current

legislation in order to heighten profits or create a competitive advantage.

In many ways, this is what gave rise to subprime lending in the first place, with

legislation being fairly weak (if not unaware) in terms of loan packaging and debt swaps. As

long as the explicit regulations were recognized, lenders use any remaining slack to grow the

bottom line. Though a separate issue, the introduction of social data into lending decisions piques

many of the same concerns. If regulations state that a lender cannot discriminate potential

borrowers due to their race, sexual orientation, national origin, and so forth, then it may be

considered enough to refrain from explicitly doing so in an openly malicious manner. However,

that “letter of the law” today doesn’t necessarily prevent a lender from creating a data-based

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algorithm, being based off of social data that uses personal information and that of their network

of social contacts in order to determine their credit worthiness. Lenders would simply need to

walk the tightrope, so to speak, as many have done for generations in the name of profits and

business continuity.

The above example highlights the ever-present “dubious legality” in the lending industry,

requiring direct regulatory requirements while using any remaining wiggle room in order to gain

a financial edge. Lenders may argue, though, that this is essence of a business. Higher revenues

and lower costs drive higher profits and returns for all parties involved. What right does the

public have to define how businesses operate or how they work to optimize their lending

practices? So, if no laws are being violated, what’s the real problem? Consider the following

role-play between a lender and potential borrower:

Lender: “We’ve reviewed your file, complete with social information and details of your

technological use as of late, and we’ve come to a decision.”

Borrower: “You reviewed what? What does my social status have to do with my lending

status?”

Lender: “Your background and social activity can offer some insights into your

perceived strengths and weaknesses as a potential borrower.”

Borrower: “So, what? My pictures, friends, and family are all fair game? And my phone,

too?”

Lender: “In no way would we ever discriminate based on any gender, religious, or racial

aspects during a lending decision. The practice simply offers new insights into your

history than a credit score alone.”

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Borrower: “If there’s no discrimination, why is the rate you’re offering to me different

than what it would be from my FICO credit score, by itself?”

Though a seemingly extreme example, there is a significant potential for this very

predicament to present itself if the practice of using social data goes awry. And if we put

ourselves in the shoes of the borrower, what are they to do at that point? If they’re fed up and

decide to walk across the street to a different bank, what’s to say that the next lender won’t be

using the very same practice? Or who is to say the other lender would extend credit on traditional

factors? It’s in the nature of a business to observe competitor practices and to adapt with

practices of their own, thus combatting a competitor’s advantage in the market. What’s to stop it

from happening if the practice is legal and in compliance with current regulations, but still has a

hint of unethical reasoning embedded within its foundation?

One efficient manner in which to use the social data, as previously discussed, is the

generation of algorithms in order to process data and spit out a lending recommendation as an

aggregate of the data consumed. In one way, algorithms can be seen as an unbiased decision-

making process that simply follows finite instructions in order to reach a conclusion or end

result. To rest on this, however, would be irresponsible from a risk perspective. One must also

recognize that algorithms are developed by people in the industry, injecting industry insights and

procedural logic along the way.

So, while an algorithm may be an unbiased processor, what can be said about its

designer(s) and their original logic? In the same manner as a statistical test, if a hypothesis or

experimental design is biased in its construction, then any results from that process can be

flawed, as well. Therefore, any bias or discrimination that leaks into “industry logic” could

eventually flow into the design or implementation of their algorithms. This is yet another

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example of how a lender, though in compliance with lending regulation, can potentially be

executing in an ethically “gray area”. It is a constant game of tug-of-war between a consumer’s

quest to save money and a business’s quest to increase their yield.

Public vs. Private Capital

A broad departure in the lending risk environment can come from a lender’s decision to

use either public capital or private capital in order to back their financial instruments. The

manner in which these businesses operate, are led, and handle financial decisions can be wildly

different. In some ways, it all depends on the source of financial backing and the regulatory

requirements related to their business’s structure. A large majority of the banks hit the hardest by

the subprime lending fiasco over the last decade operated as public entities. In most cases, they

were public investment firms that were traded on the New York Stock Exchange (i.e., Goldman

Sachs, Wells Fargo, Wachovia, Merrill Lynch, etc.).23 Any lending, selling of packaged loans, or

credit default swaps were regularly backed by the Federal Deposit Insurance Corporation (FDIC)

insurance policy to some degree. Public funds and publically-owned debt were frequently

packaged as marketable financial instruments.24 As such, this spread some of the procedural and

financial risks onto the public, rather than on the business itself. The Federal Government does

what it can to protect the public from poor business practices, but the big banks of the world

simply have to high of volume to follow around with a magnifying glass.

Private capital, on the other hand, presents a more internally risk-based environment, and

as such, can result in lower government scrutiny in their lending practices. For this reason, we

have seen a jump in recent years in the number of private equity lenders entering the market. The

23 Labaton, Stephen. “Agency’s ’04 Rule Let Banks Pile Up New Debt”, The New York Times.

http://www.nytimes.com/2008/10/03/business/03sec.html. October 2, 2008. 24 Federal Deposit Insurance Corporation. https://www.fdic.gov/about/index.html#1. 2015.

21

idea behind private capital presents the potential for lower government involvement and lessened

reliance on the public to define its business practices. This is not to say that private equity firms

can do whatever they want, but instead, reflects a simple distinction in the placement of risk. For

a private equity firm, lending decisions and financial instruments are backed by existing private

capital, rather than governmental or public funds. Lending Club is a fantastic example of this

model, with peer-to-peer lending that is backed by privately-held investments and not federal

deposit insurance. As lending determinations become more and more complex (or creative,

depending upon your view), we will likely continue to see private equity firms enter the market

in hopes of employing more sophisticated operating models to the industry.

A New Model Proposition

Existing regulatory models for financial institutions, while extensive, are ill-equipped to

deal with an incredibly dynamic environment like data-based consumer lending. There are

simply too many facets within the industry for a single regulatory body to monitor all aspects of

the lending process, especially with the advent of social data feeding into lending determinations.

Where legal requirements may be fully obliged, there are many privacy-related and ethical

implications stemming from the latest trends in lending analytics. As seen with many other

industries, it is common for innovation and procedural creativity to outpace legal and ethical

frameworks. Uber and Lyft are perfect examples of private industry outpacing public regulations.

With this thought in mind, an overarching ethical framework should be constructed in a manner

that forces business to add transparency and accountability to their operations.

The following model is somewhat akin to that which is employed for the Sarbanes-Oxley

Act of 2002, which was developed in response to financial reporting and fraud-related scandals

22

in public business like Enron and Worldcom.25 The proposed model below is intended to be used

in both public and private lending environments. Simply put, the model involves a cyclical

course of action that allows for accountability and transparency, as well as opportunities to

examine lending practices and adapt to the needs of a dynamic industry.

The process is intended to be iterative, as doing so allows for the business to adapt while

potentially gaining as much from the process as those that seek heightened control and

transparency in lending practices. Instead of an internal control design, however, this process

would involve the formal documentation of social data usage from all aspects of the business.

This would include the retrieval and storage of data along with any subsequent analytics and

decision-making guidance that stems from the analysis.

The first phase of the process would involve a requirement to fully document the design

of its data and decision making model from womb to tomb. As much as it would involve a visual

flow of information through the process, it should also include a written description of what data

is being gathered, what data is being used, how data is being used, and how data is being secured

with retention plans. One must recognize, though, that many lenders could be skeptical about

documenting their processes too intensely, as it could indicate or hint at the source of any

25 107 Congress Public Law 204 (Sarbanes-Oxley).http://www.gpo.gov/fdsys/pkg/PLAW-

107publ204/html/PLAW-107publ204.htm. 2002.

23

competitive advantage and give other lenders an edge. The nature of this process is that it would

be universally applied to the lending community and that any minute, proprietary details could

be maintained. It is more of a focus on the commitment to fair and honest lending, free of bias or

discriminatory treatment. The design documents created during this phase would be made

available to potential borrowers in considering business with the lender, in order to ensure that

consumers are given full disclosure in route to an informed decision.

While some institutions could argue that their terms of service documents, which are

available online for many leading institutions, detail this information today, there is a significant

difference in the degree of the detail being discussed. Many terms of service are exceedingly

vague, which allows for a lot of underlying flexibility in how that agreement is applied in the

lending process. Zest Finance, for example, is an innovative payday lending organization whose

process is rooted deeply in the analytics of big data. Their website details their motto of “All

Data is Credit Data.”26 Furthermore, their policy indicates the following: “With a team of some

of the world’s best data scientists from Google and lending experts from Capital One,

ZestFinance analyzes thousands of potential credit variables – everything from financial

information to technology usage – to better assess factors like the potential for fraud, the risk of

default, and the viability of a long-term customer relationship.” What is not evident, however, is

to what extent the data gathering process will go and how it will be used.

Again, one recognizes that some of the answers to this question would indicate

proprietary logic. What is being proposed here is for financial institutions to bridge the gap.

Meeting consumers in the middle to explain what data is being used and to what extent, without

26 ZestFinance, “How We Do It”. http://www.zestfinance.com/how-we-do-it.html. 2012.

24

revealing the proprietary calculations in detail, could offer a great deal of transparency into the

existing fairness and justice in the lending process.

The second phase of implementation involves executive leadership as well as internal

functions, ensuring that the process is put into place according to the documents created during

the design phase. Without a directed commitment to transparency and follow-through during

implementation, the design phase could be rendered moot and borrowers could return to the dark,

where lending practices and handling of personal data is largely unknown or ambiguously

reported.

The third phase of monitoring is one that is most beneficial to the lending organization, as

it is an internal function and allows for the identification of procedural opportunities and

improvements. If through the course of normal business, management becomes aware of an

adverse reaction to the treatment of certain social data sets, a formal monitoring task would be

essential in identifying opportunities to improve processes and react to consumer demands as

quickly as possible. In the same vein, if social data is being gathered, but not utilized to its

potential in a lending determination algorithm, a monitoring effort would allow for adjustments

to be made in a clear and efficient manner. Also, an iterative process of this nature ensures that it

can be utilized as a continuous improvement method by cycling through the process in

perpetuity, increasing awareness and accountability to its customers. It eases notions like

informed consent and documents the usage of data in its processes so that before entering into an

agreement, consumers can fully understand (at least to a reasonable extent) the implications of

the process and how a determination was made. Though an extremely delicate matter, the

proposed process would benefit all parties involved with increased transparency, reliability, and

consumer understanding in the lending industry.

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Conclusion

Data Science has the potential to extend credit to a greater number of people and provide

access to credit markets to many underserved individuals. However, a focus on ethical and legal

concerns regarding these credit decisions needs to occur before such technology can truly go

mainstream. We propose extending existing legal and ethical frameworks through iterative

review processes that incorporates existing legislation but allows room for innovation in lending.

Additionally, we advocate continued application of existing laws to ensure equitable and fair

access to credit markets for all individuals.

26

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